Genetic Algorithm Modeling with GPU Parallel Computing Technology

TL;DR

This paper introduces a GPU-accelerated genetic algorithm that significantly improves processing speed and scalability, leveraging parallel computing technology for data mining and astrophysical data classification.

Contribution

The paper presents a novel GPU-based implementation of a genetic algorithm, enhancing performance and scalability over previous CPU-based models.

Findings

Achieved significant speedup in genetic algorithm processing.

Demonstrated scalability with large astrophysical datasets.

Validated the model's effectiveness in data classification tasks.

Abstract

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.